PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (60)
 

Clipboard (0)
None
Journals
Year of Publication
1.  Organization-wide adoption of computerized provider order entry systems: a study based on diffusion of innovations theory 
Background
Computerized provider order entry (CPOE) systems have been introduced to reduce medication errors, increase safety, improve work-flow efficiency, and increase medical service quality at the moment of prescription. Making the impact of CPOE systems more observable may facilitate their adoption by users. We set out to examine factors associated with the adoption of a CPOE system for inter-organizational and intra-organizational care.
Methods
The diffusion of innovation theory was used to understand physicians' and nurses' attitudes and thoughts about implementation and use of the CPOE system. Two online survey questionnaires were distributed to all physicians and nurses using a CPOE system in county-wide healthcare organizations. The number of complete questionnaires analyzed was 134 from 200 nurses (67.0%) and 176 from 741 physicians (23.8%). Data were analyzed using descriptive-analytical statistical methods.
Results
More nurses (56.7%) than physicians (31.3%) stated that the CPOE system introduction had worked well in their clinical setting (P < 0.001). Similarly, more physicians (73.9%) than nurses (50.7%) reported that they found the system not adapted to their specific professional practice (P = < 0.001). Also more physicians (25.0%) than nurses (13.4%) stated that they did want to return to the previous system (P = 0.041). We found that in particular the received relative advantages of the CPOE system were estimated to be significantly (P < 0.001) higher among nurses (39.6%) than physicians (16.5%). However, physicians' agreements with the compatibility of the CPOE and with its complexity were significantly higher than the nurses (P < 0.001).
Conclusions
Qualifications for CPOE adoption as defined by three attributes of diffusion of innovation theory were not satisfied in the study setting. CPOE systems are introduced as a response to the present limitations in paper-based systems. In consequence, user expectations are often high on their relative advantages as well as on a low level of complexity. Building CPOE systems therefore requires designs that can provide rather important additional advantages, e.g. by preventing prescription errors and ultimately improving patient safety and safety of clinical work. The decision-making process leading to the implementation and use of CPOE systems in healthcare therefore has to be improved. As any change in health service settings usually faces resistance, we emphasize that CPOE system designers and healthcare decision-makers should continually collect users' feedback about the systems, while not forgetting that it also is necessary to inform the users about the potential benefits involved.
doi:10.1186/1472-6947-9-52
PMCID: PMC2809050  PMID: 20043843
2.  The use of mobile phones as a data collection tool: A report from a household survey in South Africa 
Background
To investigate the feasibility, the ease of implementation, and the extent to which community health workers with little experience of data collection could be trained and successfully supervised to collect data using mobile phones in a large baseline survey
Methods
A web-based system was developed to allow electronic surveys or questionnaires to be designed on a word processor, sent to, and conducted on standard entry level mobile phones.
Results
The web-based interface permitted comprehensive daily real-time supervision of CHW performance, with no data loss. The system permitted the early detection of data fabrication in combination with real-time quality control and data collector supervision.
Conclusions
The benefits of mobile technology, combined with the improvement that mobile phones offer over PDA's in terms of data loss and uploading difficulties, make mobile phones a feasible method of data collection that needs to be further explored.
doi:10.1186/1472-6947-9-51
PMCID: PMC2811102  PMID: 20030813
3.  An e-health driven laboratory information system to support HIV treatment in Peru: E-quity for laboratory personnel, health providers and people living with HIV 
Background
Peru has a concentrated HIV epidemic with an estimated 76,000 people living with HIV (PLHIV). Access to highly active antiretroviral therapy (HAART) expanded between 2004-2006 and the Peruvian National Institute of Health was named by the Ministry of Health as the institution responsible for carrying out testing to monitor the effectiveness of HAART. However, a national public health laboratory information system did not exist. We describe the design and implementation of an e-health driven, web-based laboratory information system - NETLAB - to communicate laboratory results for monitoring HAART to laboratory personnel, health providers and PLHIV.
Methods
We carried out a needs assessment of the existing public health laboratory system, which included the generation and subsequent review of flowcharts of laboratory testing processes to generate better, more efficient streamlined processes, improving them and eliminating duplications. Next, we designed NETLAB as a modular system, integrating key security functions. The system was implemented and evaluated.
Results
The three main components of the NETLAB system, registration, reporting and education, began operating in early 2007. The number of PLHIV with recorded CD4 counts and viral loads increased by 1.5 times, to reach 18,907. Publication of test results with NETLAB took an average of 1 day, compared to a pre-NETLAB average of 60 days. NETLAB reached 2,037 users, including 944 PLHIV and 1,093 health providers, during its first year and a half. The percentage of overall PLHIV and health providers who were aware of NETLAB and had a NETLAB password has also increased substantially.
Conclusion
NETLAB is an effective laboratory management tool since it is directly integrated into the national laboratory system and streamlined existing processes at the local, regional and national levels. The system also represents the best possible source of timely laboratory information for health providers and PLHIV, allowing patients to access their own results and other helpful information about their health, extending the scope of HIV treatment beyond the health facility and providing a model for other countries to follow. The NETLAB system now includes 100 diseases of public health importance for which the Peruvian National Institute of Health and the network of public health laboratories provide testing and results.
doi:10.1186/1472-6947-9-50
PMCID: PMC2797768  PMID: 20003281
4.  Improving follow-up of abnormal cancer screens using electronic health records: trust but verify test result communication 
Background
Early detection of colorectal cancer through timely follow-up of positive Fecal Occult Blood Tests (FOBTs) remains a challenge. In our previous work, we found 40% of positive FOBT results eligible for colonoscopy had no documented response by a treating clinician at two weeks despite procedures for electronic result notification. We determined if technical and/or workflow-related aspects of automated communication in the electronic health record could lead to the lack of response.
Methods
Using both qualitative and quantitative methods, we evaluated positive FOBT communication in the electronic health record of a large, urban facility between May 2008 and March 2009. We identified the source of test result communication breakdown, and developed an intervention to fix the problem. Explicit medical record reviews measured timely follow-up (defined as response within 30 days of positive FOBT) pre- and post-intervention.
Results
Data from 11 interviews and tracking information from 490 FOBT alerts revealed that the software intended to alert primary care practitioners (PCPs) of positive FOBT results was not configured correctly and over a third of positive FOBTs were not transmitted to PCPs. Upon correction of the technical problem, lack of timely follow-up decreased immediately from 29.9% to 5.4% (p < 0.01) and was sustained at month 4 following the intervention.
Conclusion
Electronic communication of positive FOBT results should be monitored to avoid limiting colorectal cancer screening benefits. Robust quality assurance and oversight systems are needed to achieve this. Our methods may be useful for others seeking to improve follow-up of FOBTs in their systems.
doi:10.1186/1472-6947-9-49
PMCID: PMC2797509  PMID: 20003236
5.  Development of a validation algorithm for 'present on admission' flagging 
Background
The use of routine hospital data for understanding patterns of adverse outcomes has been limited in the past by the fact that pre-existing and post-admission conditions have been indistinguishable. The use of a 'Present on Admission' (or POA) indicator to distinguish pre-existing or co-morbid conditions from those arising during the episode of care has been advocated in the US for many years as a tool to support quality assurance activities and improve the accuracy of risk adjustment methodologies. The USA, Australia and Canada now all assign a flag to indicate the timing of onset of diagnoses. For quality improvement purposes, it is the 'not-POA' diagnoses (that is, those acquired in hospital) that are of interest.
Methods
Our objective was to develop an algorithm for assessing the validity of assignment of 'not-POA' flags. We undertook expert review of the International Classification of Diseases, 10th Revision, Australian Modification (ICD-10-AM) to identify conditions that could not be plausibly hospital-acquired. The resulting computer algorithm was tested against all diagnoses flagged as complications in the Victorian (Australia) Admitted Episodes Dataset, 2005/06. Measures reported include rates of appropriate assignment of the new Australian 'Condition Onset' flag by ICD chapter, and patterns of invalid flagging.
Results
Of 18,418 diagnosis codes reviewed, 93.4% (n = 17,195) reflected agreement on status for flagging by at least 2 of 3 reviewers (including 64.4% unanimous agreement; Fleiss' Kappa: 0.61). In tests of the new algorithm, 96.14% of all hospital-acquired diagnosis codes flagged were found to be valid in the Victorian records analysed. A lower proportion of individual codes was judged to be acceptably flagged (76.2%), but this reflected a high proportion of codes used <5 times in the data set (789/1035 invalid codes).
Conclusion
An indicator variable about the timing of occurrence of diagnoses can greatly expand the use of routinely coded data for hospital quality improvement programmes. The data-cleaning instrument developed and tested here can help guide coding practice in those health systems considering this change in hospital coding. The algorithm embodies principles for development of coding standards and coder education that would result in improved data validity for routine use of non-POA information.
doi:10.1186/1472-6947-9-48
PMCID: PMC2793244  PMID: 19951430
6.  1842676957299765Latent class cluster analysis to understand heterogeneity in prostate cancer treatment utilities 
Background
Men with prostate cancer are often challenged to choose between conservative management and a range of available treatment options each carrying varying risks and benefits. The trade-offs are between an improved life-expectancy with treatment accompanied by important risks such as urinary incontinence and erectile dysfunction. Previous studies of preference elicitation for prostate cancer treatment have found considerable heterogeneity in individuals' preferences for health states given similar treatments and clinical risks.
Methods
Using latent class mixture model (LCA), we first sought to understand if there are unique patterns of heterogeneity or subgroups of individuals based on their prostate cancer treatment utilities (calculated time trade-off utilities for various health states) and if such unique subgroups exist, what demographic and urological variables may predict membership in these subgroups.
Results
The sample (N = 244) included men with prostate cancer (n = 188) and men at-risk for disease (n = 56). The sample was predominantly white (77%), with mean age of 60 years (SD ± 9.5). Most (85.9%) were married or living with a significant other. Using LCA, a three class solution yielded the best model evidenced by the smallest Bayesian Information Criterion (BIC), substantial reduction in BIC from a 2-class solution, and Lo-Mendell-Rubin significance of < .001. The three identified clusters were named high-traders (n = 31), low-traders (n = 116), and no-traders (n = 97). High-traders were more likely to trade survival time associated with treatment to avoid potential risks of treatment. Low-traders were less likely to trade survival time and accepted risks of treatment. The no-traders were likely to make no trade-offs in any direction favouring the status quo. There was significant difference among the clusters in the importance of sexual activity (Pearson's χ2 = 16.55, P = 0.002; Goodman and Kruskal tau = 0.039, P < 0.001). In multinomial logistic regression, the level of importance assigned to sexual activity remained an independent predictor of class membership. Age and prostate cancer/at-risk status were not significant factors in the multinomial model.
Conclusion
Most existing utility work is undertaken focusing on how people choose on average. Distinct clusters of prostate cancer treatment utilities in our sample point to the need for further understanding of subgroups and need for tailored assessment and interventions.
doi:10.1186/1472-6947-9-47
PMCID: PMC2789058  PMID: 19941668
7.  The feasibility of a web-based counselling program for occupational physicians and employees on sick leave due to back or neck pain 
Background
The objective of this feasibility study was to gain insight into occupational physicians' (OPs) and employees' use of, and attitudes towards, 'Snelbeter' (Get Well Fast), a new web-based counselling program for employees on sick leave due to non-specific back or neck pain and their OPs.
Methods
Registered user information was collected from the website to get insight in the use of the program by employees (n = 24). Qualitative information was obtained through semi-structured in-depth interviews with 19 OPs and nine employees in order to get insight in the actual use of the provided information, the attitudes towards the program and possible improvements of the program.
Results
Actual use of the program among OPs was low. The majority of OPs, eight out of 11 (73%), never or only occasionally signed in. The greatest obstacle for OPs to use the program was the low number of eligible employees involved. Employees appreciated the program but their use was moderate. A small majority of the employees who used the program, 14 out of 24 (58%), opened 50% to 100% of the provided documents, a majority of the interviewed employees, seven out of nine (78%), used the provided information sometimes or regularly. The absence of personal contact was found to be a major barrier towards use of the program by employees.
Conclusion
Although both OPs and employees appreciated the idea of the program and employees appreciated using it, program utilization was moderate to low. The discussion section reveals that before implementation can be started to any extent, the program will need adaptations that make it more attractive to use. The program should be considered for both return to work (RTW) and the prevention of sick leave. Adding personal contact (e.g. involving physiotherapists) to the program may also be promising.
doi:10.1186/1472-6947-9-46
PMCID: PMC2779794  PMID: 19895689
8.  Unified wavelet and gaussian filtering for segmentation of CT images; application in segmentation of bone in pelvic CT images 
Background
The analysis of pelvic CT scans is a crucial step for detecting and assessing the severity of Traumatic Pelvic Injuries. Automating the processing of pelvic CT scans could impact decision accuracy, decrease the time for decision making, and reduce health care cost. This paper discusses a method to automate the segmentation of bone from pelvic CT images. Accurate segmentation of bone is very important for developing an automated assisted-decision support system for Traumatic Pelvic Injury diagnosis and treatment.
Methods
The automated method for pelvic CT bone segmentation is a hierarchical approach that combines filtering and histogram equalization, for image enhancement, wavelet analysis and automated seeded region growing. Initial results of segmentation are used to identify the region where bone is present and to target histogram equalization towards the specific area. Speckle Reducing Anisotropic Didffusion (SRAD) filter is applied to accentuate the desired features in the region. Automated seeded region growing is performed to refine the initial bone segmentation results.
Results
The proposed method automatically processes pelvic CT images and produces accurate segmentation. Bone connectivity is achieved and the contours and sizes of bones are true to the actual contour and size displayed in the original image. Results are promising and show great potential for fracture detection and assessing hemorrhage presence and severity.
Conclusion
Preliminary experimental results of the automated method show accurate bone segmentation. The novelty of the method lies in the unique hierarchical combination of image enhancement and segmentation methods that aims at maximizing the advantages of the combined algorithms. The proposed method has the following advantages: it produces accurate bone segmentation with maintaining bone contour and size true to the original image and is suitable for automated bone segmentation from pelvic CT images.
doi:10.1186/1472-6947-9-S1-S8
PMCID: PMC2773923  PMID: 19891802
9.  Measuring the effect of commuting on the performance of the Bayesian Aerosol Release Detector 
Background
Early detection of outdoor aerosol releases of anthrax is an important problem. The Bayesian Aerosol Release Detector (BARD) is a system for detecting releases of aerosolized anthrax and characterizing them in terms of location, time and quantity. Modelling a population's exposure to aerosolized anthrax poses a number of challenges. A major difficulty is to accurately estimate the exposure level--the number of inhaled anthrax spores--of each individual in the exposed region. Partly, this difficulty stems from the lack of fine-grained data about the population under surveillance. To cope with this challenge, nearly all anthrax biosurveillance systems, including BARD, ignore the mobility of the population and assume that exposure to anthrax would occur at one's home administrative unit--an assumption that limits the fidelity of the model.
Methods
We employed commuting data provided by the U.S. Census Bureau to parameterize a commuting model. Then, we developed methods for integrating commuting into BARD's simulation and detection algorithms and conducted two studies to measure the effect. The first study (simulation study) was designed to assess how BARD's detection and characterization performance are impacted by incorporation of commuting in BARD's outbreak-simulation algorithm. The second study (detection study) was designed to measure the effect of incorporating commuting in BARD's outbreak-detection algorithm.
Results
We found that failing to account for commuting in detection (when commuting is present in simulation) leads to a deterioration in BARD's detection and characterization performance that is both statistically and practically significant. We found that a simplified approach to accounting for commuting in detection--simplified to maintain tractability of inference--nearly fully restored both detection and characterization performance of BARD detector.
Conclusion
We conclude that it is important to account for commuting (and mobility in general) in BARD's simulation algorithm. Further, the proposed method for incorporating commuting in BARD's detection algorithm can successfully perform the necessary correction in the detection algorithm, while preserving BARD's practicality. In our future work, we intend to further study the problem of the trade-off between running time and accuracy of the computation in BARD's version that includes commuting and ultimately find the best such trade-off.
doi:10.1186/1472-6947-9-S1-S7
PMCID: PMC2773922  PMID: 19891801
10.  Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia 
Background
Functional Magnetic Resonance Imaging (fMRI) has been proven to be useful for studying brain functions. However, due to the existence of noise and distortion, mapping between the fMRI signal and the actual neural activity is difficult. Because of the difficulty, differential pattern analysis of fMRI brain images for healthy and diseased cases is regarded as an important research topic. From fMRI scans, increased blood ows can be identified as activated brain regions. Also, based on the multi-sliced images of the volume data, fMRI provides the functional information for detecting and analyzing different parts of the brain.
Methods
In this paper, the capability of a hierarchical method that performed an optimization algorithm based on modified maximum model (MCM) in our previous study is evaluated. The optimization algorithm is designed by adopting modified maximum correlation model (MCM) to detect active regions that contain significant responses. Specifically, in the study, the optimization algorithm is examined based on two groups of datasets, dyslexia and healthy subjects to verify the ability of the algorithm that enhances the quality of signal activities in the interested regions of the brain. After verifying the algorithm, discrete wavelet transform (DWT) is applied to identify the difference between healthy and dyslexia subjects.
Results
We successfully showed that our optimization algorithm improves the fMRI signal activity for both healthy and dyslexia subjects. In addition, we found that DWT based features can identify the difference between healthy and dyslexia subjects.
Conclusion
The results of this study provide insights of associations of functional abnormalities in dyslexic subjects that may be helpful for neurobiological identification from healthy subject.
doi:10.1186/1472-6947-9-S1-S6
PMCID: PMC2773921  PMID: 19891800
11.  BioDEAL: community generation of biological annotations 
Background
Publication databases in biomedicine (e.g., PubMed, MEDLINE) are growing rapidly in size every year, as are public databases of experimental biological data and annotations derived from the data. Publications often contain evidence that confirm or disprove annotations, such as putative protein functions, however, it is increasingly difficult for biologists to identify and process published evidence due to the volume of papers and the lack of a systematic approach to associate published evidence with experimental data and annotations. Natural Language Processing (NLP) tools can help address the growing divide by providing automatic high-throughput detection of simple terms in publication text. However, NLP tools are not mature enough to identify complex terms, relationships, or events.
Results
In this paper we present and extend BioDEAL, a community evidence annotation system that introduces a feedback loop into the database-publication cycle to allow scientists to connect data-driven biological concepts to publications.
Conclusion
BioDEAL may change the way biologists relate published evidence with experimental data. Instead of biologists or research groups searching and managing evidence independently, the community can collectively build and share this knowledge.
doi:10.1186/1472-6947-9-S1-S5
PMCID: PMC2773920  PMID: 19891799
12.  Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching 
Background
Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.
Methods
First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases.
Results
Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images.
Conclusion
The experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.
doi:10.1186/1472-6947-9-S1-S4
PMCID: PMC2773919  PMID: 19891798
13.  An intelligent listening framework for capturing encounter notes from a doctor-patient dialog 
Background
Capturing accurate and machine-interpretable primary data from clinical encounters is a challenging task, yet critical to the integrity of the practice of medicine. We explore the intriguing possibility that technology can help accurately capture structured data from the clinical encounter using a combination of automated speech recognition (ASR) systems and tools for extraction of clinical meaning from narrative medical text. Our goal is to produce a displayed evolving encounter note, visible and editable (using speech) during the encounter.
Results
This is very ambitious, and so far we have taken only the most preliminary steps. We report a simple proof-of-concept system and the design of the more comprehensive one we are building, discussing both the engineering design and challenges encountered. Without a formal evaluation, we were encouraged by our initial results. The proof-of-concept, despite a few false positives, correctly recognized the proper category of single-and multi-word phrases in uncorrected ASR output. The more comprehensive system captures and transcribes speech and stores alternative phrase interpretations in an XML-based format used by a text-engineering framework. It does not yet use the framework to perform the language processing present in the proof-of-concept.
Conclusion
The work here encouraged us that the goal is reachable, so we conclude with proposed next steps.
Some challenging steps include acquiring a corpus of doctor-patient conversations, exploring a workable microphone setup, performing user interface research, and developing a multi-speaker version of our tools.
doi:10.1186/1472-6947-9-S1-S3
PMCID: PMC2773918  PMID: 19891797
14.  A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images 
Background
Traumatic pelvic injuries are often associated with severe, life-threatening hemorrhage, and immediate medical treatment is therefore vital. However, patient prognosis depends heavily on the type, location and severity of the bone fracture, and the complexity of the pelvic structure presents diagnostic challenges. Automated fracture detection from initial patient X-ray images can assist physicians in rapid diagnosis and treatment, and a first and crucial step of such a method is to segment key bone structures within the pelvis; these structures can then be analyzed for specific fracture characteristics. Active Shape Model has been applied for this task in other bone structures but requires manual initialization by the user. This paper describes a algorithm for automatic initialization and segmentation of key pelvic structures - the iliac crests, pelvic ring, left and right pubis and femurs - using a hierarchical approach that combines directed Hough transform and Active Shape Models.
Results
Performance of the automated algorithm is compared with results obtained via manual initialization. An error measures is calculated based on the shapes detected with each method and the gold standard shapes. ANOVA results on these error measures show that the automated algorithm performs at least as well as the manual method. Visual inspection by two radiologists and one trauma surgeon also indicates generally accurate performance.
Conclusion
The hierarchical algorithm described in this paper automatically detects and segments key structures from pelvic X-rays. Unlike various other x-ray segmentation methods, it does not require manual initialization or input. Moreover, it handles the inconsistencies between x-ray images in a clinical environment and performs successfully in the presence of fracture. This method and the segmentation results provide a valuable base for future work in fracture detection.
doi:10.1186/1472-6947-9-S1-S2
PMCID: PMC2773917  PMID: 19891796
15.  AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM 
Background
Digital mammography is one of the most promising options to diagnose breast cancer which is the most common cancer in women. However, its effectiveness is enfeebled due to the difficulty in distinguishing actual cancer lesions from benign abnormalities, which results in unnecessary biopsy referrals. To overcome this issue, computer aided diagnosis (CADx) using machine learning techniques have been studied worldwide. Since this is a classification problem and the number of features obtainable from a mammogram image is infinite, a feature selection method that is tailored for use in the CADx systems is needed.
Methods
We propose a feature selection method based on multiple support vector machine recursive feature elimination (MSVM-RFE). We compared our method with four previously proposed feature selection methods which use support vector machine as the base classifier. Experiments were performed on lesions extracted from the Digital Database of Screening Mammography, the largest public digital mammography database available. We measured average accuracy over 5-fold cross validation on the 8 datasets we extracted.
Results
Selecting from 8 features, conventional algorithms like SVM-RFE and multiple SVM-RFE showed slightly better performance than others. However, when selecting from 22 features, our proposed modified multiple SVM-RFE using boosting outperformed or was at least competitive to all others.
Conclusion
Our modified method may be a possible alternative to SVM-RFE or the original MSVM-RFE in many cases of interest. In the future, we need a specific method to effectively combine models trained during the feature selection process and a way to combine feature subsets generated from individual SVM-RFE instances.
doi:10.1186/1472-6947-9-S1-S1
PMCID: PMC2773916  PMID: 19891795
16.  Retraction: Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept 
This article [1] has been retracted because the Editors are unable to ensure the scientific veracity of the findings or the ethical conduct of the authors despite an extensive investigation.
doi:10.1186/1472-6947-9-45
PMCID: PMC2774299  PMID: 19843327
17.  Information management to enable personalized medicine: stakeholder roles in building clinical decision support 
Background
Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies.
Discussion
Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures), and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine.
Summary
This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized decision-making, a comparison of current and future applications of clinical decision support to enable individualized medical treatment plans is presented. If clinical decision support tools are to impact outcomes in a clear and positive manner, their development and deployment must therefore consider the needs of the providers, including specific practice needs, information workflow, and practice environment.
doi:10.1186/1472-6947-9-44
PMCID: PMC2763860  PMID: 19814826
18.  An interdisciplinary team communication framework and its application to healthcare 'e-teams' systems design 
Background
There are few studies that examine the processes that interdisciplinary teams engage in and how we can design health information systems (HIS) to support those team processes. This was an exploratory study with two purposes: (1) To develop a framework for interdisciplinary team communication based on structures, processes and outcomes that were identified as having occurred during weekly team meetings. (2) To use the framework to guide 'e-teams' HIS design to support interdisciplinary team meeting communication.
Methods
An ethnographic approach was used to collect data on two interdisciplinary teams. Qualitative content analysis was used to analyze the data according to structures, processes and outcomes.
Results
We present details for team meta-concepts of structures, processes and outcomes and the concepts and sub concepts within each meta-concept. We also provide an exploratory framework for interdisciplinary team communication and describe how the framework can guide HIS design to support 'e-teams'.
Conclusion
The structures, processes and outcomes that describe interdisciplinary teams are complex and often occur in a non-linear fashion. Electronic data support, process facilitation and team video conferencing are three HIS tools that can enhance team function.
doi:10.1186/1472-6947-9-43
PMCID: PMC2753306  PMID: 19754966
19.  A global approach to the management of EMR (Electronic Medical Records) of patients with HIV/AIDS in Sub-Saharan Africa: the experience of DREAM Software 
Background
The DREAM Project operates within the framework of the national health systems of several sub-Saharan African countries and aims to introduce the essential components of an integrated strategy for the prevention and treatment of HIV/AIDS. The project is intended to serve as a model for a wide-ranging scale-up in the response to the epidemic. This paper aims to show DREAM's challenges and the solutions adopted. One of the solutions is the efficient management of the clinical data regarding the treatment of the patients and epidemiological analyses.
Methods
Specific software for the management of the patients' EMR has been created within the DREAM programme in order to deal with the challenges deriving from the context in which DREAM operates. Setting up a computer infrastructure in health centres, providing a power supply, as well as managing the data and the project resources efficiently and reliably, are some of the questions that have been analysed in this study.
Results
Over the years this software has proved that it is able to respond to the need for efficient management of the clinical data and organization of the health centres. Today it is used in 10 countries in sub-Saharan Africa by thousands of professionals and by now it has reached its fourth version. The medical files of over 73,000 assisted patients are managed by this software and the data collected with it have become essential for the epidemiological research that is carried out to improve the effectiveness of the therapy.
Conclusion
Sub-Saharan Africa is the region hardest hit by HIV and AIDS in the world. However, the resources and responses adopted so far, to confront the epidemic, have at times been rather minimalist. The DREAM project has faced the battle against the epidemic by equipping itself with qualitative standards comparable to Western ones. The experience of DREAM has revealed that it is indeed possible to guarantee levels of excellence in developing countries, also in the sphere of ICT (Information and Communication Technology), thus making the intervention even more effective and contributing to bridging the digital divide.
doi:10.1186/1472-6947-9-42
PMCID: PMC2749819  PMID: 19747371
20.  Privacy-preserving record linkage using Bloom filters 
Background
Combining multiple databases with disjunctive or additional information on the same person is occurring increasingly throughout research. If unique identification numbers for these individuals are not available, probabilistic record linkage is used for the identification of matching record pairs. In many applications, identifiers have to be encrypted due to privacy concerns.
Methods
A new protocol for privacy-preserving record linkage with encrypted identifiers allowing for errors in identifiers has been developed. The protocol is based on Bloom filters on q-grams of identifiers.
Results
Tests on simulated and actual databases yield linkage results comparable to non-encrypted identifiers and superior to results from phonetic encodings.
Conclusion
We proposed a protocol for privacy-preserving record linkage with encrypted identifiers allowing for errors in identifiers. Since the protocol can be easily enhanced and has a low computational burden, the protocol might be useful for many applications requiring privacy-preserving record linkage.
doi:10.1186/1472-6947-9-41
PMCID: PMC2753305  PMID: 19706187
21.  Improving healthcare consumer effectiveness: An Animated, Self-serve, Web-based Research Tool (ANSWER) for people with early rheumatoid arthritis 
Background
People with rheumatoid arthritis (RA) should use DMARDs (disease-modifying anti-rheumatic drugs) within the first three months of symptoms in order to prevent irreversible joint damage. However, recent studies report the delay in DMARD use ranges from 6.5 months to 11.5 months in Canada. While most health service delivery interventions are designed to improve the family physician's ability to refer to a rheumatologist and prescribe treatments, relatively little has been done to improve the delivery of credible, relevant, and user-friendly information for individuals to make treatment decisions. To address this care gap, the Animated, Self-serve, Web-based Research Tool (ANSWER) will be developed and evaluated to assist people in making decisions about the use of methotrexate, a type of DMARD. The objectives of this project are: 1) to develop ANSWER for people with early RA; and 2) to assess the extent to which ANSWER reduces people's decisional conflict about the use of methotrexate, improves their knowledge about RA, and improves their skills of being 'effective healthcare consumers'.
Methods/design
Consistent with the International Patient Decision Aid Standards, the development process of ANSWER will involve: 1.) creating a storyline and scripts based on the best evidence on the use of methotrexate and other management options in RA, and the contextual factors that affect a patient's decision to use a treatment as found in ERAHSE; 2.) using an interactive design methodology to create, test, analyze and refine the ANSWER prototype; 3.) testing the content and user interface with health professionals and patients; and 4.) conducting a pilot study with 51 patients, who are diagnosed with RA in the past 12 months, to assess the extent to which ANSWER improves the quality of their decisions, knowledge and skills in being effective consumers.
Discussion
We envision that the ANSWER will help accelerate the dissemination of knowledge and skills necessary for people with early RA to make informed choices about treatment and to manage their health. The latest in animation and online technology will ensure ANSWER fills a knowledge translation gap, focusing on the next generation of people living with RA.
doi:10.1186/1472-6947-9-40
PMCID: PMC2733893  PMID: 19695086
22.  Disease surveillance using a hidden Markov model 
Background
Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.
Methods
A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.
Results
Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.
Conclusion
Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.
doi:10.1186/1472-6947-9-39
PMCID: PMC2735038  PMID: 19664256
23.  Access to electronic health records by care setting and provider type: perceptions of cancer care providers in Ontario, Canada 
Background
The use of electronic health records (EHRs) to support the organization and delivery of healthcare is evolving rapidly. However, little is known regarding potential variation in access to EHRs by provider type or care setting. This paper reports on observed variation in the perceptions of access to EHRs by a wide range of cancer care providers covering diverse cancer care settings in Ontario, Canada.
Methods
Perspectives were sought regarding EHR access and health record completeness for cancer patients as part of an internet survey of 5663 cancer care providers and administrators in Ontario. Data were analyzed using a multilevel logistic regression model. Provider type, location of work, and access to computer or internet were included as covariates in the model.
Results
A total of 1997 of 5663 (35%) valid responses were collected. Focusing on data from cancer care providers (N = 1247), significant variation in EHR access and health record completeness was observed between provider types, location of work, and level of computer access. Providers who worked in community hospitals were half as likely as those who worked in teaching hospitals to have access to their patients' EHRs (OR 0.45 95% CI: 0.24–0.85, p < 0.05) and were six times less likely to have access to other organizations' EHRs (OR 0.15 95% CI: 0.02–1.00, p < 0.05). Compared to surgeons, nurses (OR 3.47 95% CI: 1.80–6.68, p < 0.05), radiation therapists/physicists (OR 7.86 95% CI: 2.54–25.34, p < 0.05), and other clinicians (OR 4.92 95% CI: 2.15–11.27, p < 0.05) were more likely to report good access to their organization's EHRs.
Conclusion
Variability in access across different provider groups, organization types, and geographic locations illustrates the fragmented nature of EHR adoption in the cancer system. Along with focusing on technological aspects of EHR adoption within organizations, it is essential that there is cross-organizational and cross-provider access to EHRs to ensure patient continuity of care, system efficiency, and high quality care.
doi:10.1186/1472-6947-9-38
PMCID: PMC2746184  PMID: 19664247
24.  Physicians' attitudes towards ePrescribing – evaluation of a Swedish full-scale implementation 
Background
The penetration rate of Electronic Health Record (EHR) systems in health care is increasing. However, many different EHR-systems are used with varying ePrescription designs and functionalities. The aim of the present study was to evaluate experienced ePrescribers' attitudes towards ePrescribing for suggesting improvements.
Methods
Physicians (n = 431) from seven out of the 21 Swedish health care regions, using one of the six most widely implemented EHR-systems with integrated electronic prescribing modules, were recruited from primary care centers and hospital clinics of internal medicine, orthopaedics and surgery. The physicians received a web survey that comprised eight questions on background data and 19 items covering attitudes towards ePrescribing. Forty-two percent (n = 199) of the physicians answered the questionnaire; 90% (n = 180) of the respondents met the inclusion criteria and were included in the final analysis.
Results
A majority of the respondents regarded their EHR-system easy to use in general (81%), and for the prescribing of drugs (88%). Most respondents believed they were able to provide the patients better service by ePrescribing (92%), and regarded ePrescriptions to be time saving (91%) and to be safer (83%), compared to handwritten prescriptions. Some of the most frequently reported weaknesses were: not clearly displayed price of drugs (43%), complicated drug choice (21%), and the perception that it was possible to handle more than one patient at a time when ePrescribing (13%). Moreover, 62% reported a lack of receipt from the pharmacy after successful transmission of an ePrescription. Although a majority (73%) of the physicians reported that they were always or often checking the ePrescription a last time before transmitting, 25% declared that they were seldom or never doing a last check. The respondents suggested a number of improvements, among others, to simplify the drug choice and the cancellation of ePrescriptions.
Conclusion
The Swedish physicians in the group studied were generally satisfied with their specific EHR-system and with ePrescribing as such. However, identified weaknesses warrant improvements of the EHR-systems as well as of their implementation in the individual health care organisation.
doi:10.1186/1472-6947-9-37
PMCID: PMC2732618  PMID: 19664219
25.  FluDetWeb: an interactive web-based system for the early detection of the onset of influenza epidemics 
Background
The early identification of influenza outbreaks has became a priority in public health practice. A large variety of statistical algorithms for the automated monitoring of influenza surveillance have been proposed, but most of them require not only a lot of computational effort but also operation of sometimes not-so-friendly software.
Results
In this paper, we introduce FluDetWeb, an implementation of a prospective influenza surveillance methodology based on a client-server architecture with a thin (web-based) client application design. Users can introduce and edit their own data consisting of a series of weekly influenza incidence rates. The system returns the probability of being in an epidemic phase (via e-mail if desired). When the probability is greater than 0.5, it also returns the probability of an increase in the incidence rate during the following week. The system also provides two complementary graphs. This system has been implemented using statistical free-software (ℝ and WinBUGS), a web server environment for Java code (Tomcat) and a software module created by us (Rdp) responsible for managing internal tasks; the software package MySQL has been used to construct the database management system. The implementation is available on-line from: http://www.geeitema.org/meviepi/fludetweb/.
Conclusion
The ease of use of FluDetWeb and its on-line availability can make it a valuable tool for public health practitioners who want to obtain information about the probability that their system is in an epidemic phase. Moreover, the architecture described can also be useful for developers of systems based on computationally intensive methods.
doi:10.1186/1472-6947-9-36
PMCID: PMC2732617  PMID: 19640304

Results 1-25 (60)